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                <identifier>oai:data.mendeley.com/2s75kgnzd7.2</identifier>
                <datestamp>2026-04-20T18:29:05Z</datestamp>
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            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>Marquez, Renny</dc:creator>
    <dc:title>Synthetic disjoint and overlapping dynamic attributed networks with ground-truth information</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>1. Synthetic network 1: Using DANCer, we created 12 dynamic undirected attributed datasets (10 timestamps) with homogeneous communities. Over time, nodes increase from 256 to 1868, communities from 5 to 10, and edges from 1000 to 11905.

2. Synthetic network 2: 200-node graphs over 20 snapshots, starting as two 100-node groups. At a random step, 40% (Dataset 1) or 80% (Dataset 2) of nodes migrate to a new community. Generated via SBM (0.3 intra-community, 0.1 inter-community probability).

3. Synthetic network 3: Ten timestamps with growing/shrinking communities (three groups of 80, 90 and, 100 nodes), via a degree-corrected SBM for the network structure. The proposed attributes assess strongly assortative (Dataset 1), weakly assortative (Dataset 2), and disassortative (Dataset 3) structures.

4. Overlapping synthetic networks: Using Greene&apos;s benchmark, we create 11 overlapping datasets with 10 snapshots. Each network has 250 nodes, overlapping nodes from 0 to 50, and overlapping memberships from 2 to 25. Specific kinds of changes in communities of a dynamic network were simulated:

- Birth and death: Two birth events per time step and two death events per time step. Dataset 1 has no overlapping nodes; Dataset 2 has five overlapping nodes and two overlapping memberships; Dataset 3 has ten overlapping nodes and two overlapping memberships; Dataset 4 has 25 overlapping nodes and two overlapping memberships; Dataset 5 has 50 overlapping nodes and two overlapping memberships; Dataset 6 has five overlapping nodes and five overlapping memberships; Dataset 7 has five overlapping nodes and ten overlapping memberships; Dataset 8 has five overlapping nodes and 25 overlapping memberships.

- Merge and split: Two merge events per time step and two split events per time step. Dataset 9 has five overlapping nodes and two overlapping memberships.

- Expansion and contraction: Two expansion events per time step and two contraction events per time step, with a rate of 0.1. Dataset 10 has five overlapping nodes and two overlapping memberships.

- Node switching between communities: A probability of 0.2 for a node to switch its community membership. Dataset 11 has five overlapping nodes and two overlapping memberships.

5. Real-world network: The crime network comprises individuals who committed crimes in Chile between 2019 and 2021. The edges of the graph denote joint participation in a case. To construct the timesteps, the date of the crime was used to establish three-month intervals, where each period is non-overlapping. The attributes considered in this dataset are the crimes committed by each subject, where each column designates a specific crime and a value of 1 indicates that the individual committed the corresponding crime. Robbery in an uninhabited place, robbery with violence, and homicide in a fight, fall under the scope of this network, which includes a total of 26 crime types. In this dataset, the attribute values are time-varying.</dc:description>
    <dc:subject>Data Science</dc:subject>
    <dc:subject>Clustering</dc:subject>
    <dc:subject>Social Network</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/2s75kgnzd7.2</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/2s75kgnzd7.2</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/2s75kgnzd7</dc:relation>
    <dc:date>2026-04-20T18:29:05Z</dc:date>
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